ACEMAGIC M1A Pro (i9-13900HK + ARC A770)

The specs that matter for inference are VRAM capacity, memory bandwidth, and compute throughput. Here’s how the M1A Pro breaks down:

512 GB/s is the critical number. For context, an RTX 4060 laptop GPU has ~256 GB/s; an RTX 4090 desktop has ~1 TB/s. The M1A Pro sits in the middle — good enough for real-time chat at 7B–13B, but not for high-throughput serving of large models. With llama.cpp and Intel’s Vulkan backend, expect:

• Llama 3.1 8B (Q4_K_M): ~25–30 tok/s
• Mistral 7B (Q8): ~20–25 tok/s
• Qwen 2.5 14B (Q4_K_M): ~12–15 tok/s
• DeepSeek-R1 7B (Q4): ~22–28 tok/s

These are plausible for a 512 GB/s GPU with XMX support. Performance will vary based on prompt length, batch size, and software stack. Using Intel’s IPEX-LLM (which optimizes for XMX) can yield 10–20% higher throughput than generic Vulkan.

At 300W peak, the M1A Pro is not a low-power edge device. But for a mini PC with a discrete GPU, it’s reasonable. Idle power is around 40W. If you’re deploying at the edge with limited cooling or battery, consider a lower-TDP alternative. For a desk setup, the trade-off is acceptable for the inference performance.

These models fit entirely on the GPU at the specified quantization, avoiding system-RAM offload and maximizing speed:

• Llama 3.1 8B – Q8 (8 GB) or Q4_K_M (5.5 GB)
• Mistral 7B – Q8 (7 GB) or Q4_K_M (4.5 GB)
• Qwen 2.5 7B – Q8 (7 GB)
• DeepSeek-R1 7B – Q8 (7 GB)
• Gemma 2 9B – Q4_K_M (5.5 GB)
• Phi-3 14B – Q4_K_M (~8 GB)
• Llama 3.1 13B – Q5_K_M (~10 GB) – sweet spot for quality and speed
• Qwen 2.5 14B – Q4_K_M (~8 GB)

With 32 GB DDR5, you can run larger models by offloading some layers to system RAM. Expect slower generation (5–10 tok/s) but still usable for interactive tasks:

• Llama 3.1 32B – Q3_K_M (~16 GB GPU + 8 GB system) – feasible, ~5–8 tok/s
• Qwen 2.5 32B – Q3_K_M (~16 GB GPU + 8 GB system)
• Mixtral 8x7B – Q4_K_M (~24 GB total) – requires full offload, ~3–5 tok/s
• DeepSeek-R1 32B – Q3_K_M (~16 GB GPU + 8 GB system)

The M1A Pro can run vision-language models (e.g., LLaVA 1.6 7B, Qwen-VL 7B) entirely on GPU. For long-context tasks (32K+ tokens), the 32 GB system RAM helps with KV cache offload, but generation speed will drop. The six-display output (USB4 8K + 2x DP 2.0 8K + 2x HDMI 2.0) makes it ideal for multi-monitor AI development — run your model on one screen, monitor logs on another, and output on a third.

For most practitioners, the best quality-to-speed tradeoff is Llama 3.1 8B at Q4_K_M or Qwen 2.5 14B at Q4_K_M. Both fit in VRAM, produce high-quality outputs, and deliver interactive speeds (20+ tok/s). If you need 13B parameter reasoning, use Q5_K_M — still fits and gives better coherence than Q4.

• Hobbyists running local chatbots – You want a plug-and-play machine for Llama 3.1, Mistral, or DeepSeek. The M1A Pro runs llama.cpp, Ollama, and LM Studio out of the box. 16 GB VRAM is enough for most 7B–13B models at decent quantization.
• Developers building AI-powered applications – If you’re prototyping agentic workflows (e.g., LangChain, CrewAI) and need a dedicated inference box that’s quiet enough for a desk, this fits. The i9 CPU handles prompt processing and orchestration while the GPU runs inference.
• Teams running small inference servers – For low-throughput internal tools (e.g., code assistants, document Q&A), the M1A Pro can serve 1–2 concurrent requests. Use vLLM or llama.cpp server mode. Not for production-scale serving, but fine for dev/test.
• Edge deployment scenarios – The compact form factor and low idle power make it suitable for kiosks, digital signage with AI, or on-premises inference in retail/warehouse settings. The 2.5GbE LAN and USB4 allow fast data transfer.
• Multi-monitor AI development – Six display outputs let you run a model, monitor GPU stats, and view results simultaneously. Ideal for debugging and visualization-heavy workflows.

• Training from scratch – 16 GB VRAM is insufficient for fine-tuning most 7B+ models with LoRA. You can do QLoRA on smaller models (7B) with limited batch size, but a desktop RTX 4090 (24 GB) or cloud GPU is better.
• High-throughput serving – For >10 concurrent users, you need a server GPU (A100, H100) or at least a 24 GB RTX 3090/4090.
• Low-noise environments – Under sustained inference, the fans are audible. If silence is critical, consider a fanless Mac Mini or a laptop with passive cooling.

The ROG Flow Z13 is a 2-in-1 laptop with a similar CPU and an RTX 4060 (8 GB VRAM, 256 GB/s). The M1A Pro offers double the VRAM and double the memory bandwidth — a clear advantage for LLM inference. The Flow Z13 is portable; the M1A Pro stays on your desk. If you need mobility, pick the Flow. If you need to run 13B models or larger quantizations, the M1A Pro wins.

The Mac Mini M4 Pro offers 24 GB unified memory (shared CPU/GPU) with ~400 GB/s bandwidth. For models that fit in 24 GB (e.g., Llama 3.1 8B at Q8, Mixtral 8x7B at Q3), Apple’s Metal backend can match or exceed Intel Arc performance. The M1A Pro is cheaper ($799 vs ~$1,400 for M4 Pro) and runs Windows-native tools like llama.cpp with CUDA-like performance via Vulkan. The Mac Mini is quieter and more power-efficient. Choose the M1A Pro if you need Windows, prefer Intel tooling, or want to run models that require >16 GB but <32 GB via offload.

• You need 16 GB VRAM at this price point ($799). No other mini PC offers a discrete GPU with this memory capacity.
• You want a compact, dedicated inference machine that doesn’t take up desk space.
• You prefer Intel’s ecosystem (OpenVINO, IPEX-LLM) or need Windows for specific AI frameworks.
• You require six display outputs for multi-monitor dev work.